Mary Tyree

Future dryness in the southwest US and the hydrology of the early 21st century drought.

Recently the Southwest has experienced a spate of dryness, which presents a challenge to the sustainability of current water use by human and natural systems in the region. In the Colorado River Basin, the early 21st century drought has been the most extreme in over a century of Colorado River flows, and might occur in any given century with probability of only 60%. However, hydrological model runs from downscaled Intergovernmental Panel on Climate Change Fourth Assessment climate change simulations suggest that the region is likely to become drier and experience more severe droughts than this. In the latter half of the 21st century the models produced considerably greater drought activity, particularly in the Colorado River Basin, as judged from soil moisture anomalies and other hydrological measures. As in the historical record, most of the simulated extreme droughts build up and persist over many years. Durations of depleted soil moisture over the historical record ranged from 4 to 10 years, but in the 21st century simulations, some of the dry events persisted for 12 years or more. Summers during the observed early 21st century drought were remarkably warm, a feature also evident in many simulated droughts of the 21st century. These severe future droughts are aggravated by enhanced, globally warmed temperatures that reduce spring snowpack and late spring and summer soil moisture. As the climate continues to warm and soil moisture deficits accumulate beyond historical levels, the model simulations suggest that sustaining water supplies in parts of the Southwest will be a challenge.

Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.

Impact of climate variation on mosquito abundance in California.

ABSTRACT Temporal variation in the abundance of the encephalitis virus vector mosquito, Culex tarsalis Coquillet, was linked significantly with coincident and antecedent measures of regional climate, including temperature, precipitation, snow pack, and the El Niño/Southern Oscillation anomaly. Although variable among traps, historical records that spanned two to five decades revealed climate influences on spring and summer mosquito abundance as early as the previous fall through early summer. Correlations between winter and spring precipitation and snow pack and spring Cx. tarsalis abundance were stronger than correlations with summer abundance. Spring abundance was also correlated positively with winter and spring temperature, whereas summer abundance correlated negatively with spring temperature and not significantly with summer temperature. Correlations with antecedent climate provide the opportunity to forecast vector abundance and herefore encephalitis virus risk, a capability useful in intervention decision support systems at local and state levels.

The Key Role of Heavy Precipitation Events in Climate Model Disagreements of Future Annual Precipitation Changes in California

AbstractClimate model simulations disagree on whether future precipitation will increase or decrease over California, which has impeded efforts to anticipate and adapt to human-induced climate change. This disagreement is explored in terms of daily precipitation frequency and intensity. It is found that divergent model projections of changes in the incidence of rare heavy (>60 mm day−1) daily precipitation events explain much of the model disagreement on annual time scales, yet represent only 0.3% of precipitating days and 9% of annual precipitation volume. Of the 25 downscaled model projections examined here, 21 agree that precipitation frequency will decrease by the 2060s, with a mean reduction of 6–14 days yr−1. This reduces Californias mean annual precipitation by about 5.7%. Partly offsetting this, 16 of the 25 projections agree that daily precipitation intensity will increase, which accounts for a model average 5.3% increase in annual precipitation. Between these conflicting tendencies, 12 projecti...

Seasonality of Kawasaki Disease: A Global Perspective

Background Understanding global seasonal patterns of Kawasaki disease (KD) may provide insight into the etiology of this vasculitis that is now the most common cause of acquired heart disease in children in developed countries worldwide. Methods Data from 1970-2012 from 25 countries distributed over the globe were analyzed for seasonality. The number of KD cases from each location was normalized to minimize the influence of greater numbers from certain locations. The presence of seasonal variation of KD at the individual locations was evaluated using three different tests: time series modeling, spectral analysis, and a Monte Carlo technique. Results A defined seasonal structure emerged demonstrating broad coherence in fluctuations in KD cases across the Northern Hemisphere extra-tropical latitudes. In the extra-tropical latitudes of the Northern Hemisphere, KD case numbers were highest in January through March and approximately 40% higher than in the months of lowest case numbers from August through October. Datasets were much sparser in the tropics and the Southern Hemisphere extra-tropics and statistical significance of the seasonality tests was weak, but suggested a maximum in May through June, with approximately 30% higher number of cases than in the least active months of February, March and October. The seasonal pattern in the Northern Hemisphere extra-tropics was consistent across the first and second halves of the sample period. Conclusion Using the first global KD time series, analysis of sites located in the Northern Hemisphere extra-tropics revealed statistically significant and consistent seasonal fluctuations in KD case numbers with high numbers in winter and low numbers in late summer and fall. Neither the tropics nor the Southern Hemisphere extra-tropics registered a statistically significant aggregate seasonal cycle. These data suggest a seasonal exposure to a KD agent that operates over large geographic regions and is concentrated during winter months in the Northern Hemisphere extra-tropics.

The Impact of Climate Change on Air Quality–Related Meteorological Conditions in California. Part I: Present Time Simulation Analysis

AbstractThis study investigates the impacts of climate change on meteorology and air quality conditions in California by dynamically downscaling Parallel Climate Model (PCM) data to high resolution (4 km) using the Weather Research and Forecast (WRF) model. This paper evaluates the present years’ (2000–06) downscaling results driven by either PCM or National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) reanalysis data. The analyses focused on the air quality–related meteorological variables, such as planetary boundary layer height (PBLH), surface temperature, and wind. The differences of the climatology from the two sets of downscaling simulations and the driving global datasets were compared, which illustrated that most of the biases of the downscaling results were inherited from the driving global climate model (GCM). The downscaling process added mesoscale features but also introduced extra biases into the driving global data. The main source of bias in the PCM data is an...

Future climate: projected average

Global climate models (GCMs) are the fundamental drivers of regional climate-change projections (IPCC 2007). GCMs allow us to characterize changes in atmospheric circulation associated with human causes at global and continental scales. However, because of the planetary scope of the GCMs, their resolution, or level of detail, is somewhat coarse. A typical GCM grid spacing is about 62 miles (100 km) or greater, which is inadequate for creating projections and evaluating impacts of climate change at a regional scale. Thus, a “downscaling” procedure is needed to provide finer spatial detail of the model results.

Simulation of 20th century temperature trends

Results are reported from simulations with an atmospheric general circulation model (GCM) covering the 20th century. These results focus primarily on the continental surface air temperature (SAT) record, with some discussion of large spatial scale precipitation averages as well. These experiments were conducted using the Max Planck Institute for Meteorology ECHAM3 GCM configured at triangular-21 truncation, giving a spatial resolution of approximately 5.5°. The surface boundary conditions for the model were constructed by forming decadal climatologies of sea surface temperature (SST) data from the Global SST Atlas (GOSSTA) data set. No changes in greenhouse gas concentrations were made during the experiments. The GCM was then integrated for 5 years with each climatology. Because the high southern latitudes are poorly sampled and show considerable high-amplitude variability in the GOSSTA data, two complete experiments were conducted. In one experiment the time-varying GOSSTA data were prescribed globally (the GLBL simulation), in the other, SSTs in the high southern latitudes were held at the 1950-1969 climatology (the NO―SH simulation). The results show both GCM simulations reproduce important features of the observed 20th century global temperature record, suggesting that the low-frequency variability in global average SAT is modulated to an important degree by variability in SSTs. In the model results, the changes in SST are transmitted to the atmosphere via latent heat flux, i.e., by changes in the flux of water through the hydrologic cycle. The most notable difference between the GLBL and NO―SH SAT records is that during the 1920-1940 period the NO―SH results agree more closely with observations, while the GLBL results are superior from the 1950s forward. At least in part, these differences appear to be linked to differences in simulated sea-ice extent in the two simulations. Analyses of large-scale averages in continental precipitation show good model-observation agreement in the tropics (±30° latitude) and in the extratropical southern hemisphere; however, the upward trend in extratropical NH continental precipitation seen in the observed record is not reproduced in either simulation.